A number of clinical imaging methodologies are available that generate grayscale images of the internal structure of the human body. Computer tomography (CT) is useful for imaging dense structures such as the skeletal system, positron emission tomography (PET) is used in conjunction with the injection of certain fluids to obtain functional information about human tumors, and magnetic resonance imaging (MRI) is used to image structures in the human body other than the skeletal structure. Each of these imaging methodologies or modalities is capable gathering three-dimensional information about the structure of the human body in multiple two dimensional slices. All of the different structures or tissue types in each of the two dimensional images are rendered in a shade of gray depending upon the absorption, reflection or nuclear spin characteristics of the different tissue types. These grayscale images are examined by a diagnostician in order to arrive at a diagnostic conclusion with respect to the location and type of pathological tissue or tumor that is present. Unfortunately, each imaging methodology has limitations. For instance, CT is most useful for imaging the skeletal structure, but not soft tissue, whereas MRI is most useful for imaging soft tissue, but may not provide the resolution or spatial detail that PET offers. To overcome this problem, it is typical to order a series of images generated by two or more of the available imaging methodologies or modalities and to place the images side-by-side and compare the results to arrive at a diagnosis. This process of examining multiple side-by-side images of the same portion of the human body generated by different imaging modalities places a large burden upon the diagnostician and, whether due to fatigue or the physiological limitation of a human's visual ability to discriminate between shades of gray (can only identify 16-32 shades), can lead to false diagnostic results. To solve this problem, diagnosticians have been led to superimpose the information contained in one grayscale image generated by one modality upon the information contained in another grayscale image generated by a second modality to create a single, composite grayscale image.
One method commonly used to register multiple grayscale images generated using different modalities is to place two or more spot markers, detectable by the imaging modalities being utilized, at locations within the human body that are to be imaged. The resultant images are then registered, one to the other, using these spot markers.
Another method for multimodal grayscale image registration is disclosed in U.S. Pat. No. 7,020,313 assigned to Mirada Solutions Limited. FIG. 8 along with the description starting in column 4, line 25 discloses a somewhat more complex method comprised of obtaining images of three modalities, namely an emission, a transmission and a structural image, enhancing the emission image, segmenting areas of the transmission image, creating a mask and applying the mask to the emission image, matching the masked emission image to the structural image and displaying the registered images.
While superimposing one grayscale image upon another grayscale image is an improvement over the side-by-side comparison technique, it is still prone to erroneous diagnosis as the merging of two or more grayscale images into a single, composite grayscale image does not necessarily enhance the visual contrast between tissue types. As a means to enhance the visual contrast between tissue types, color has been applied to grayscale images generated by different imaging modalities, such as CT, MRI and PET. Typically, enhancing the visual contrast between different tissues in this manner simplifies the job of the diagnostician and leads to a more accurate diagnostic result.
A number of methods have been employed to apply color to grayscale images. The simplest approach is to divide the grayscale into some number of shades of gray between black and white and then map a different color to each of the shades of gray. So for instance, if the gray scale is divided into 256 shades, from 0 to 255, and each color is assigned 256 shades, from 0 to 255, gray shade “0” could be assigned a red color value of “255” a green color value of “0” and a blue color value of “0” and so on with each of the other 255 gray scale shades.
Another method for color encoding a grayscale image is described in PCT publication no. WO 00/28472A1 with reference to FIG. 1 starting on page 6, line 18. FIG. 1 shows a logical flow diagram that describes a process for providing a black and white digital image including a bitmap color table and matrix of intensity values, copying the bitmap color table into memory to create a temporary bitmap color table, copying the original bitmap color table into a temporary bitmap color table to create a temporary palette array, replacing ranges of values on the temporary bitmap color table with assigned color values to create a color-modified palette array, overwriting the original bitmap color table with the color-modified palette array wile allowing the original matrix of intensity values to remain unmodified, and refreshing an image on a display device to generate a colorized image without pixel saturation. While such a color encoding method can be an aid to the diagnostic process by increasing the contrast relationships between proximate structure, a side-to-side comparison of color images generated using different modalities may still be necessary.
Another method for coloring grayscale images is disclosed in U.S. Pat. No. 7,145,336. In the summary section in column 2, starting on line 35 it is disclosed that MRI biophysical parameters of a region of interest are mapped as gray tone images and color masks are then applied to the gray tone images such that color is assigned to each of the biophysical parameters. As with the examination of multiple side-by-side grayscale images, the diagnostic process can be improved if the information contained in multiple, color images is merged into a single, composite color image. The method of this patent is primarily directed to obtaining and coloring a segmented three-dimensional rendering of a structural region and not to distinguishing any particular pathological tissue from normal tissue.
U.S. Pat. No. 5,410,250 discloses one method for producing a single color coded composite image from a plurality of multi-parameter magnetic resonance image sets. The summary section of this patent describes a method for obtaining a plurality of spatially aligned gray-tone magnetic resonance images at a plurality of predetermined pulse sequences to provide data concerning the spatial location and contrast discrimination of tissue voxels in the form of pixels having varied intensities; identifying selected regions of interest in each image representing tissues, fluids, etc.; plotting average signal intensities of pixels within each region of interest for each image according to each tissue voxel, assigning a different monochrome color to each of the images based on the signal intensities and on a desired final color rendition of a composite image formed of the plurality of images; then producing polychrome color images of the plurality of gray-tone images by combing the monochrome color with its respective image forming pixels of varying hue, saturation and intensity based on the intensity of the original gray-tone images and on the assigned monochrome colors.
U.S. Pat. No. 6,580,936 discloses another method of generating one color image from several, multi-channel MRI images. The summary section of this patent discloses that a number of MRI images are generated while varying conditions for a sample, such as a tissue sample, for which colors can be determined and for which MRI imaging is possible, then subjecting the information contain in each image to an independent component analysis to decompose the images into independent component images, selecting N points on the sample to create a training sample. This training sample is then used to generate as many transfer functions as the color components which output one color component for an arbitrary combination of independent component luminances and color components. In order to generate a color image, a number of MRI images are created while varying the conditions, and the data on the images are subjected to a second independent component analysis to generate some number of independent component images. Then, the second independent components are each calibrated to be equal to those of the first independent component, and the transfer functions obtained using the training samples are applied to the calibrated independent component images to obtain a color image.
Although the U.S. Pat. Nos. 6,580,936 and 5,410,250 patents disclose methods for combining information from multiple color images into a single composite color image, all of the information is generated using only a single modality and so any diagnostic conclusion based upon a reading of such a composite image is limited only to whatever pathological information can be identified by the single modality. Furthermore, both the -936 and -250 patents disclose a complicated method for “naturally” coloring the known, typical structures and tissue so that they can be easily recognized by a diagnostician according to these natural colors, no attention is paid to assigning a distinctive, characteristic color to pathological tissue so that it can be easily identified. Further, none of the prior art methods only identify pathological tissue with a distinctive color, rather all of the structure that is displayed in an image is colored which forces reliance on a diagnostician's skill in identifying an unnaturally occurring color among the naturally occurring colors in order to make a positive diagnosis.
Therefore, it would be advantageous if only pathological tissue in a composite image was displayed in a distinctive, characteristic color and all of the other structure displayed in the composite image is displayed in grey scale color.